Retinex-Based Fast Algorithm for Low-Light Image Enhancement

Shou-xin, Liu; Long, Wei; He, Lixin; Li, Yanyan; Ding, Wei

doi:10.3390/e23060746

Cited by 39 publications

(26 citation statements)

References 37 publications

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“…Single-scale retinex (SSR) and multiscale retinex (MSR) are two of the most well-known algorithms in retinex theory. Multiscale retinex with colour restoration (MSRCR) [ 12 ] is offered as a solution to the colour distortion problem. It did not produce satisfactory results when used in the processing of blood cell images.…”

Section: Methodsmentioning

confidence: 99%

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net

Chen

Zhu

2022

Computational and Mathematical Methods in Medicine

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To address the issues of uneven illumination and inconspicuous leukocyte properties in the gathered cell pictures, a leukocyte segmentation method based on adaptive retinex correction and U-net was proposed. The procedure begins by processing a peripheral blood image to clearly distinguish leukocytes from other components in the image. The adaptive retinex correction, which is based on multiscale retinex with colour replication (MSRCR), redefines the colour recovery function by introducing Michelson contrast. Then, the image is trained with the U-net convolutional neural network, and the leukocyte segmentation is completed. The innovation is in the manner of processing peripheral blood images, which improves the accuracy of leukocyte segmentation. This study conducts experimental evaluations on the Cellavision, BCCD, and LISC datasets. The experimental results show that the method in this study is better than the current best method, and the segmentation accuracy rate reaches 98.87%.

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Section: Methodsmentioning

confidence: 99%

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net

Chen

Zhu

2022

Computational and Mathematical Methods in Medicine

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“…Zhao et al [ 47 ] proposed a unified deep zero-reference framework termed RetinexDIP for enhancing low-light images; however, noise was not considered in the decomposition process. Liu et al [ 48 ] proposed the Retinex-based fast algorithm (RBFA) to achieve low-light image enhancement. Liang et al [ 49 ] proposed a low-light image enhancement model based on deep learning.…”

Section: Introductionmentioning

confidence: 99%

Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior

Gao

Zhang

Luo

2022

Sensors

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Low-light images are a common phenomenon when taking photos in low-light environments with inappropriate camera equipment, leading to shortcomings such as low contrast, color distortion, uneven brightness, and high loss of detail. These shortcomings are not only subjectively annoying but also affect the performance of many computer vision systems. Enhanced low-light images can be better applied to image recognition, object detection and image segmentation. This paper proposes a novel RetinexDIP method to enhance images. Noise is considered as a factor in image decomposition using deep learning generative strategies. The involvement of noise makes the image more real, weakens the coupling relationship between the three components, avoids overfitting, and improves generalization. Extensive experiments demonstrate that our method outperforms existing methods qualitatively and quantitatively.

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“…At present, the more common and typical image enhancement methods include histogram equalization and its improved algorithm, Retinex and its improved algorithm, wavelet transform method, morphology-based enhancement method, and fuzzy set-based image enhancement technology [ 11 ]. Among them, Retinex algorithm, histogram equalization, and image enhancement algorithm based on wavelet transform are widely used in medical images [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Imaging under Image Enhancement Algorithm to Analyze the Clinical Value of Placement of Drainage Tube on Incision Healing after Hepatobiliary Surgery

Yang

Wu²,

Wang³

et al. 2022

Computational and Mathematical Methods in Medicine

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This study was aimed at exploring the application value of magnetic resonance imaging (MRI) based on image enhancement algorithm in analyzing the placement of drainage tubes in the healing of incisions after hepatobiliary surgery. A total of 70 patients with liver cancer undergoing laparoscopic hepatobiliary surgery were selected, including 34 males and 36 females. According to the detection method of postoperative recovery, they were divided into a group A (conventional MRI detection) and a group B (MRI detection based on Retinex algorithm). Patients were divided into two groups according to whether subcutaneous drainage tubes were placed: group C (no subcutaneous drainage tubes were placed) and group D (subcutaneous drainage tubes were placed), with 35 patients in each group. The results showed that there was no significant difference between group A and group B in tumor residual or recurrence. The detection rate of tumor capsule in group B was significantly higher than that in group A ( P < 0.05 ). The sensitivity, specificity, and accuracy of group A for the detection of recurrent lesions were 63.40%, 86.90%, and 78.60%, respectively; those in group B were 82.70%, 98.50%, and 93.20%, respectively. Therefore, the difference between the two groups was statistically significant ( P < 0.05 ). The incidence of poor wound healing and infection in group C were significantly lower than those in group D ( P < 0.05 ). Therefore, the effect of MRI detection based on image enhancement algorithm was more conducive to the evaluation of postoperative recovery due to the traditional MRI detection. In addition, the drainage tube was helpful to the postoperative wound healing and showed high clinical value.

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Retinex-Based Fast Algorithm for Low-Light Image Enhancement

Cited by 39 publications

References 37 publications

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net

Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior

Magnetic Resonance Imaging under Image Enhancement Algorithm to Analyze the Clinical Value of Placement of Drainage Tube on Incision Healing after Hepatobiliary Surgery

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